When multiple reservoirs are produced through a common facility network, the capability to integrate the modeling of surface and subsurface can be critical to field development and optimization. The shared facility network imposes constraints that the combined production cannot exceed, determines the pressure drop in the flow lines, and the composition and volume of the sales and reinjection streams. Pressure drop in flow lines is particularly important in deepwater field development, where flow lines are long, and production from multiple reservoirs can flow through the same riser.
Often, the fluid characterizations of these reservoirs have been derived independently. In each case, the appropriate fluid representation was selected that provided an optimum combination of accuracy and computational efficiency. The two most common fluid characterizations are the equation of state (EOS) model and the black oil model. Examples of common EOS models in the industry include the Peng-Robinson EOS (Peng, Robinson, 1976) and the Soave-Redlich-Kwong EOS (Soave, 1972).
A hydrocarbon fluid may actually be composed of hundreds of distinct components. When modeling using an EOS, the engineer must specify the number of pseudo-components (typically 5 to 12) and their EOS properties. Pseudo-components are combinations of actual components. Alternatively, black-oil modeling involves specification of a number of common engineering measurements in tables that vary with pressure. However, it is inherently a model with two pseudo-components. The net result is that the different connected reservoirs are being modeled with a variable number of pseudo-components, some of which may be common. However, even the common pseudo-components may have different fluid properties in the different reservoirs.
Several examples have been presented in the literature, including from Ghorayeb et al. in 2003 and Ghorayeb and Holmes in 2005, for various approaches in which the black oil models are first converted to a common compositional model and then an EOS is used to calculate the fluid properties. However, these approach lead to an EOS model with a large number of components that is extremely computationally expensive to solve.
The illustrated figures are only exemplary and are not intended to assert or imply any limitation with regard to the environment, architecture, design, or process in which different embodiments may be implemented.